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LMVD-ID: a4b03e8d
Paper published June 23, 2026
Entry analyzed July 20, 2026
Paper-reported evidence
Confidence: Source-linked

The LMVD-ID is an internal research identifier, not an official CVE identifier.

Long-Term Memory Authority Laundering

The paper describes a reproducible agent-memory poisoning issue: untrusted content stored in persistent memory can later influence consequential actions after its origin is laundered through agent summarization…

Read primary paperBibTeX citation

Paper-evaluated models(8)

GPT-5 Chat, GPT-4o Mini, Claude Opus 4.1 +5 more
  • GPT-5 Chat
  • GPT-4o Mini
  • Claude Opus 4.1
  • Claude Sonnet 4.5
  • Gemini 2.5 Flash
  • Llama 4 Maverick
  • DeepSeek Chat
  • Qwen 3 235B-A22B

Description

The paper describes a reproducible agent-memory poisoning issue: untrusted content stored in persistent memory can later influence consequential actions after its origin is laundered through agent summarization, trusted-tool echo, or manufactured corroboration. The paper reports that content scoring, lineage, and single-session capability/IFC defenses remain vulnerable because they decide authority from malleable content or derivation signals. This is a paper-reported evaluation; it was not independently reproduced here.

Examples

See the primary source for the paper's evaluation and defensive reproduction context: https://arxiv.org/abs/2606.24322 (opens in a new tab)

Impact

A poisoned memory can steer later payments, purchases, setting changes, tool selection, or data egress across sessions. In the paper’s unified benchmark, the reported laundering ASR was 68% for trust scoring and capability/IFC and 47% for lineage, while the proposed origin-bound monitor reported 0% ASR with 100% legitimate utility. These are author-reported measurements, not independently verified facts.

Affected Systems

  • LLM agents with persistent long-term memory
  • Memory systems that authorize actions using content trust scores or malleable lineage
  • Agents that summarize untrusted inputs into memory
  • Agents that pass untrusted memory through trusted tools
  • Mem0-backed agent memory in the paper’s evaluation

Mitigation Steps

  • Bind memory authority to authenticated write-time origin rather than inferred content or mutable lineage.
  • Propagate the lowest-integrity origin label through summarization, consolidation, retrieval, and tool-call boundaries.
  • Treat trusted-tool output as untrusted when any security-relevant input derives from untrusted memory.
  • Gate consequential actions on trusted-derived values, independent trusted corroboration, or fresh action-bound user authorization.
  • Require corroborators to have distinct authenticated identities, administrative domains, and upstream data sources; do not count repeated untrusted claims.
  • Use single-use authorization bound to the exact action and security-relevant value to prevent replay or substitution.
  • Maintain tamper-evident audit logs for writes, elevations, and allow/deny decisions.

Evidence

  • Primary source abstract: https://arxiv.org/abs/2606.24322 (opens in a new tab) — states that persistent memory poisoning can steer later payments, setting changes, or data exfiltration, and identifies summarization, trusted-tool echo, and manufactured corroboration as laundering channels.
  • Primary source Sections I–II: https://arxiv.org/html/2606.24322 (opens in a new tab) — defines the cross-session write-retrieve-act issue, gives a defensive example of a poisoned web-derived note later steering data egress, and scopes the adversary to untrusted external content.
  • Primary source Section V: https://arxiv.org/html/2606.24322 (opens in a new tab) — defines MEM-INV-Bench across 12 domains, five consequential tool types, direct and laundering attacks, and eight frontier models.
  • Primary source Section VI, Table I: https://arxiv.org/html/2606.24322 (opens in a new tab) — reports pooled direct/laundering ASR and utility: none 84/68/100, trust_score 0/68/100, lineage 0/47/100, capability_ifc 84/68/100, and TMA-NM 0/0/100.
  • Primary source Section VI, Tables II–III: https://arxiv.org/html/2606.24322 (opens in a new tab) — reports per-model and pooled consequential-action results for gpt-5-chat, gpt-4o-mini, Claude, Gemini, Llama, DeepSeek, and Qwen models.
  • Primary source Sections III–IV: https://arxiv.org/html/2606.24322 (opens in a new tab) — specifies write-time origin binding, non-malleable propagation, independent corroboration or fresh action-bound authorization, and the bounded TLA+ security claims.
  • Directly linked artifact README: https://github.com/yedidel/mem-inv-bench (opens in a new tab) — provides synthetic benchmark scenarios, logged results, offline monitor tests, and TLA+ models; the repository states the scenarios use fictitious accounts and recipients for authorized defensive evaluation. Artifact existence was checked, but its results were not rerun independently.

Research context and confidence

Evidence and verification
Paper-reported; independent reproduction is not documented.
Primary source plus a dedicated evidence section.
Severity
Not rated by this catalog.
Source and publication type
arXiv · Research preprint.
Peer-review status is not provided by this source.
Author and publication status
Author metadata is not stored; see the primary paper.
Threat model and attacker access
Ability to influence untrusted model inputs or connected content.
Related deployment categories
Agent workflows; Agent memory
Taxonomy labels only; paper-specific deployment prerequisites are not inferred.
Affected systems
LLM agents with persistent long-term memory Memory systems that authorize actions using content trust scores or malleable lineage Agents that summarize untrusted inputs into memory Agents that pass untrusted memory…

Research Paper

Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees

Primary source: arXiv. Findings are reported by the cited research and have not been independently verified.

View Paper